Particle swarm optimised fuzzy method for prediction of water table elevation fluctuation Online publication date: Thu, 21-Jun-2018
by Shilpa Jain; Dinesh C.S. Bisht; Prakash Chandra Mathpal
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 10, No. 2, 2018
Abstract: Particle swarm optimisation (PSO) is a population-based powerful evolutionary computational technique inspired by social behaviour simulation of bird flocking and fish schooling. PSO has been applied successfully to a wide range of applications like scheduling, artificial neural networks (ANN) training, control strategy determination and ingredient mix optimisation. Fuzzy logic can easily cope up with vagueness and uncertainty in time series data. This has been applied for prediction of water table elevation, in our earlier work and results are quite promising. But, the optimisation of length of fuzzy intervals was a big constraint for researchers. In this research paper, the optimal length of fuzzy intervals in the universe of discourse is been selected using particle swarm optimisation. The results obtained after applying this combined approach to prediction of water table elevation are better than the previous method.
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